| Challenge: | Multi-task learning has been frustrated by the interference among tasks. |
| Approach: | They propose a capsule-based multi-task learning architecture which is unified, simple and effective. |
| Outcome: | The proposed model can cluster features for each task in the network, which helps reduce the interference among tasks. |
Similar Papers
Investigating Capsule Networks with Dynamic Routing for Text Classification (D18-1)
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| Challenge: | Earlier efforts in text modeling have achieved limited success on word meanings . convolutional neural networks (CNNs) are used to model higher level concepts and facts in texts . |
| Approach: | They propose three strategies to stabilize dynamic routing process to alleviate disturbance of noise capsules. |
| Outcome: | The proposed methods achieve state-of-the-art on 4 out of 6 datasets . they show that capsule networks exhibit significant improvement over baseline methods . |
Gated Multi-Task Network for Text Classification (N18-2)
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| Challenge: | Existing approaches to multitask learning share the features without distinguishing the usefulness of the features, generating undesired interference between tasks. |
| Approach: | They propose to introduce a gate mechanism into multi-task CNN and propose a new gated sharing unit which can filter the feature flows between tasks and greatly reduce the interference. |
| Outcome: | The proposed approach can learn selection rules automatically and gain a great improvement over strong baselines. |
Learning What to Share: Leaky Multi-Task Network for Text Classification (C18-1)
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| Challenge: | Existing approaches to multi-task learning suffer from the interference between tasks because they lack selection mechanism for feature sharing. |
| Approach: | They propose a multi-task convolutional neural network with the Leaky Unit which has memory and forgetting mechanism to filter the feature flows between tasks. |
| Outcome: | The proposed model can filter feature flows between tasks and improve performance on five datasets. |
Attention-Based Capsule Networks with Dynamic Routing for Relation Extraction (D18-1)
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| Challenge: | Existing neural networks focus on instance representation, and subsampling fails to retain precise spatial relationships between higher-level parts. |
| Approach: | They propose a neural approach based on capsule networks with attention mechanisms to extract relational information from a capsule. |
| Outcome: | The proposed method improves the precision of the predicted relations with different benchmarks. |
Investigating Capsule Network and Semantic Feature on Hyperplanes for Text Classification (D19-1)
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| Challenge: | Various neural networks are designed for text classification on the basis of word embedding, but polysemy is a fundamental feature of the natural language, which brings challenges to text classification. |
| Approach: | They propose to use capsule networks to construct the vectorized representation of semantics and utilize hyperplanes to decompose each capsule to acquire the specific senses. |
| Outcome: | The proposed model extracts more discriminative semantic features and yields significant performance gain compared to baseline methods. |
Capsule Network with Interactive Attention for Aspect-Level Sentiment Classification (D19-1)
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| Challenge: | Existing methods for aspect-level sentiment classification are limited for dealing with overlapped features. |
| Approach: | They propose to use capsule network to construct vector-based feature representation and cluster features by an EM routing algorithm to model semantic relationship between aspect terms and context. |
| Outcome: | The proposed model achieves state-of-the-art on three datasets. |
Hierarchical Multi-label Classification of Text with Capsule Networks (P19-2)
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| Challenge: | In hierarchical multi-label classification, samples are classified into one or multiple class labels organized in a structured label hierarchy. |
| Approach: | They apply and compare shallow capsule networks for hierarchical multi-label text classification and introduce a new real-world scenario dataset. |
| Outcome: | The proposed model outperforms neural networks and non-neural network architectures on a real-world scenario dataset. |
An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment Analysis (P19-1)
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| Challenge: | Aspect-based sentiment analysis produces a list of aspect terms and their corresponding sentiments for a sentence. |
| Approach: | They propose an interactive multi-task learning network which can learn multiple tasks simultaneously . they use a shared set of latent variables to iteratively pass information between tasks . |
| Outcome: | The proposed method outperforms existing methods on three benchmark datasets. |
Combining Deep Generative Models and Multi-lingual Pretraining for Semi-supervised Document Classification (2021.eacl-main)
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| Challenge: | Semi-supervised learning and multilingual pretraining have been shown to be effective for task-specific labelled data shortages. |
| Approach: | They propose to combine semi-supervised deep generative models and multi-lingual pretraining to form a pipeline for document classification task. |
| Outcome: | The proposed method outperforms state-of-the-art models in low-resource settings across several languages and outperformed existing models in English. |
Multi-Task Networks with Universe, Group, and Task Feature Learning (P19-1)
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| Challenge: | In multi-task learning, multiple related tasks are learned together. |
| Approach: | They propose methods that take advantage of natural groupings of related tasks . they propose parallel and serial architectures that can learn different feature spaces . |
| Outcome: | The proposed methods improve performance on natural language understanding (NLU) tasks. |